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Generalized method of moment estimation of multivariate multifractal models
Multifractal processes have recently been introduced as a new tool for modeling the stylized facts of financial markets and have been found to consistently provide certain gains in performance over basic volatility models for a broad range of assets and for various risk management purposes. Due to computational constraints, multivariate extensions of the baseline univariate multifractal framework are, however, still very sparse so far. In this paper, we introduce a parsimoniously designed multivariate multifractal model, and we implement its estimation via a Generalized Methods of Moments (GMM) algorithm. Monte Carlo studies show that the performance of this GMM estimator for bivariate and trivariate models is similar to GMM estimation for univariate multifractal models. An empirical application shows that the multivariate multifractal model improves upon the volatility forecasts of multivariate GARCH over medium to long forecast horizons.
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Journal
Economic ModellingVolume
67Pagination
136 - 148Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
ISSN
0264-9993Language
engPublication classification
C1 Refereed article in a scholarly journal; C Journal articleCopyright notice
2016 Elsevier B.V.Usage metrics
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